The analysis of amino acid coevolution has emerged as a practical method for protein structural modeling by providing structural contact information from alignments of amino acid sequences. In parallel, chemical cross‐linking/mass spectrometry (XLMS) has gained attention as a universally applicable method for obtaining low‐resolution distance constraints to model the quaternary arrangements of proteins, and more recently even protein tertiary structures. Here, we show that the structural information obtained by XLMS and coevolutionary analysis are effectively complementary: the distance constraints obtained by each method are almost exclusively associated with non‐coincident pairs of residues, and modeling results obtained by the combination of both sets are improved relative to considering the same total number of constraints of a single type. The structural rationale behind the complementarity of the distance constraints is discussed and illustrated for a representative set of proteins with different sizes and folds.

Motivation Protein structure modeling can be improved by the use of distance constraints between amino acid residues, provided such data reflects—at least partially—the native tertiary structure of the target system. In fact, only a small subset of the native contact map is necessary to successfully drive the model conformational search, so one important goal is to obtain the set of constraints with the highest true-positive rate, lowest redundancy, and greatest amount of information. In this work, we introduce a constraint evaluation and selection method based on the point-biserial correlation coefficient, which utilizes structural information from an ensemble of models to indirectly measure the power of each constraint in biasing the conformational search towards consensus structures. Results Residue contact maps obtained by direct coupling analysis are systematically improved by means of discriminant analysis, reaching in some cases accuracies often seen only in modern deep-learning based approaches. When combined with an iterative modeling workflow, the proposed constraint classification optimizes the selection of the constraint set and maximizes the probability of obtaining successful models. The use of discriminant analysis for the valorization of the information of constraint data sets is a general concept with possible applications to other constraint types and modeling problems. Availability and Implementation scripts and procedures to implement the methodology presented herein are available at https://github.com/m3g/2021_Bottino_Biserial. Supplementary information Supplementary data are available at Bioinformatics online.

Simulações de dinâmica clássica são ferramentas úteis para estudar fenômenos com bases mecânicas bem estabelecidas, tendo como únicos pré-requisitos o entendimento das soluções físicas do sistema e a descrição clara dessas soluções e seus passos para um computador por meio de uma linguagem de programação. Neste trabalho, aprendemos e exploramos uma linguagem de programação chamada Julia-desenvolvida recentemente para computação científica-por meio de uma série de exercícios nos quais resolvemos computacionalmente problemas simples de física do ensino médio. Em seguida, aplicamos os conhecimentos adquiridos para desenvolver um programa de computador baseado em dinâmica gravitacional para computar a trajetória dos planetas do sistema solar e do cometa Halley. As trajetórias resultantes exibiram tamanho, formato e período compatíveis com os dados astronômicos conhecidos, e mantiveram sua estabilidade mesmo após 70 anos de simulação. O único corpo que não apresentou o comportamento esperado foi o cometa Halley, porque a simulação não descrevia alguns fenômenos importantes para sua trajetória, como a variação de massa e interação com outros planetas.